PROJECT SUMMARY/ABSTRACT In aging, resilience to cognitive impairment is defined as better-than-predicted cognitive function as compared to typical aging individuals ? more specifically, resilient individuals exhibit performance on cognitive tasks that fall above 1.5 standard deviations of the population mean(1). The presence of as-yet-unidentified genetic factors of these resilient individuals, also referred to as ?super agers? (2), are expected to play a key role in delaying cognitive impairment. It is therefore vital that resilience genes be fully elucidated. A mechanistic understanding of the role of resilience genes could offer a route for therapeutic intervention, perhaps slowing or preventing cognitive impairment. However, attaining this goal by solely focusing on human genetics is limited because resilient individuals can be asymptomatic, passing undetected by medical geneticists. In addition, monitoring the longitudinal progression of age-related and AD-related neurobiological changes corresponding with the severity of cognitive impairment in individuals with specific mutations is virtually impossible. Brain magnetic resonance imaging (MRI) methods can directly circumvent this limitation in the field. The minimally invasive nature of MRI and its ability to capture signatures of whole brain network activity and axon/dendritic tissue architecture makes this approach ideally suited to investigate how genetic risk or resilience is associated with longitudinal progression of cognitive impairment in aging. Importantly, the ability to conduct high field imaging in normal aging mouse models that differ in their trajectory of cognitive aging to AD provides a powerful translational platform for discovery, characterization and causal inference of resilience mechanisms. We anticipate that linking brain imaging signals with the range of low-to-high risk variants in mouse models that recapitulate a spectrum of aging pathology is highly attainable and will advance the characterization and diagnostic utility of early stage neuroimaging biomarkers.